AI Visibility for media & publishing · AI Discovery Intelligence
When someone asks ChatGPT, Claude, Gemini, or Perplexity about a topic you cover, the engine composes an answer - often from your reporting - and decides whether to name you, name a competitor, or answer without a source at all. The audience that used to land on your story now reads the summary in a chat window.
28 Labs measures your citation share across the topics you cover, ties it to referral attention and subscription intent, and hands your newsroom and audience teams the moves to win it back.
4 engines · topic-scored query universe · every read confidence-tagged
Measured from the outside. No SDK in your rendering path. We do not measure training-data exposure - and we say so.
The topics where citation matters
scored
A publisher covers thousands of topics. We score each by audience and authority value and track the set where being the cited source matters most.
The shift
When someone asks an engine about your beat, it composes an answer off your property - you do not know if it cited you, a wire, or no one.
The reader who used to land on your story reads the summary instead; the attention, the subscription prompt, and the ad impression never happen.
We measure your citation share across the topics you own on all four engines, then tie movement in that share to referral attention and subscription intent.
The board-level risk
A publisher's moat is being the trusted source audiences go to in order to understand a topic. Answer engines now sit in front of that. The exposure is specific, and it is measurable.
The engine summarises your reporting well enough that the reader never clicks - the work happens, the visit does not.
A wire service or competitor gets named as the authority on a story you broke.
Aggregators and explainers get cited on your beats, eroding your standing as the place to understand them.
The engine credits the wrong outlet or repeats outdated facts in your name - an accuracy exposure for a trust business.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another publisher's numbers to make the point.
The query universe
A publisher's audience does not ask four questions. They ask thousands, across a journey. The work is not to track 50,000 random queries. It is to score every query by audience and authority value and monitor the high-value ones where being the cited source matters most, mapped to where the reader is in the journey.
Source, explainer, and depth queries on your owned beats carry the authority. Generic "what is the news" carries almost none. We weight what you track to where the authority is - so the citation-share number you read is the one that matters to the business, not a vanity average across the long tail.
The decision system
Most tools stop at "you were cited less." That is a metric, not a decision. We frame it as authority share - the share of source citations you hold against the competitor taking them - and we connect that share to the audience numbers your business already runs on.
How we keep it honest
No one can prove a single AI recommendation caused a single lead. Anyone who claims they can is selling certainty that does not exist. We treat it the way marketing-mix modeling treats a channel: we estimate the contribution from converging signals and we label the confidence of every read, so your analysts can audit any claim back to the evidence behind it.
When a read is directly observed in your own data, we say so. When it is inferred, we say so. When it rests on industry intelligence, we say that too. You never get a confident-sounding number with nothing under it.
Directly observed. The signal is present in your own analytics, subscription, or AI-engine output. We saw it happen.
Inferred. Multiple independent signals converge on the same read, but no single source confirms it outright.
Industry intelligence. The read rests on category benchmarks and external patterns, not your own data. Treated as directional.
In your stack
This is not a deck that ages the day it ships. It is a live read wired into the systems you already run, so the citation-share number and its audience impact stay current as the engines re-crawl and your competitors move.
The integrated engagement connects to your analytics, subscription, and CMS signals, so the citation-share read sits next to the audience data your teams already trust. No SDK in your rendering path.
You get a flag when your citation share on a high-value topic set moves sharply or a competitor surges, and a monthly read that tracks the trend and ties it to referral attention and subscriptions. Not once a quarter, on a slide.
HIGH, MED, or LOW on every finding, so the team acting on it knows exactly how much weight it carries before committing editorial or budget against it.
Honest about the tiers. The live read on how much of your audience movement is AI needs your first-party data - your analytics, subscription, and CMS signals. That is the integrated, enterprise engagement, not a free snapshot. A standalone audit measures your citation share across the four engines from the outside and shows where you are losing ground and to whom. The referral-and-subscription loop comes when we wire into your systems.
What winning takes
A publisher wins when it becomes the source the engines trust to answer the topic. That is structural. More articles will not do it. Being the authoritative, reachable source the engines reach for will.
We diagnose where the engines already trust you, where they trust a competitor instead, and the specific moves that shift that trust toward you. The output is a build plan your teams can act on, not a list of keywords.
Start here
A 60-minute working session. We walk a sample audit, show you how the citation-share read and the referral-and-subscription loop would shape up for your beats, and scope a live run.
No obligation. We do not measure training-data exposure, and we tell you what we can and cannot see before you commit.